tions between parameters. Engineers provide a vector
describing which measurements make sense to monitor together (for example, pressures, valve positions,
temperatures) and examples of nominal behavior.
Then IMS builds clusters based in the nominal data.

The learning process is influenced by three learningparameters: the maximum cluster radius, the initialcluster size, and the cluster growth percent. After theclusters are created, the IMS performs monitoring bycomparing the current vector data with the clustersdatabase. If the distance is 0 or close to 0, its behav-ior is considered nominal. As the distance increases,the current behavior can be considered more anom-alous. The clear advantage of IMS over the proposednovelty detection monitoring technique is that it candetect unusual behaviors in a combination of param-eters. However, it has some disadvantages withrespect to the proposed novelty detection technique:the grouping of which parameters need to be moni-tored together, apart from requiring engineeringeffort, determines the kind of anomalies the systemwill be able to detect. In this work we are concernedwith the detection of novel behaviors as soon as pos-sible even if engineers do not think this will be pos-sible. Another disadvantage is that the anomaly scoreis not intuitive; if it is 0 or close to 0 it is nominal, butit is not clear when engineers should start payingattention and performing investigations. A furtherdisadvantage is the amount of tuning that is requiredto have the IMS work properly: the weight in the vec-tor components and the values of the three learningparameters have a strong impact in the creation ofthe cluster database and, therefore, in the resultsfrom the monitoring phase.

ConclusionsWe have introduced a new monitoring paradigmbased on novelty detection. In this approach, everyday every telemetry parameter is automaticallyscanned and a list of the parameters that exhibit anovel behavior is reported to flight control engi-neers. New behaviors are often signatures of anom-alies either happening now or in the way to develop.Noticing them early is of utmost importance forplanning corrective measurements and keeping thespacecraft healthy.

A clear advantage of the proposed monitoring paradigm is the little amount of engineering effort
required: the only inputs required consist of the period duration (for example, 1 day) and ranges of times
to be used as examples of nominal behavior. During
the validation phase, users really appreciated that it
generates very few false alarms: the fact that it uses a
local density outlier detection technique avoids the
need of using a distance threshold to detect new
behaviors. Therefore, this approach does not suffer
from the problem of having to define a threshold

Figure 6. Novelty Detection Display for an Expected New Behavior.

Screen capture of the novelty detection plug-in integrated into WebMUST. It shows an expected new behavior for XMM. The highlighted
area corresponds to the period when the novel behavior was detected.